The present invention relates generally to an automotive tire pressure monitoring method and assembly and more specifically to an automotive tire pressure monitoring method and assembly with auto-location features.
Modern automotive performance relies on a plurality of individual vehicle components operating together. Although present vehicles often include new and increasingly complex components, many traditional components remain as crucial parameters in the vehicles operation. Such is the case with vehicle tire pressure. The air pressure within vehicle tires plays a vital role in tire performance and thereby effects proper vehicle operation. Interaction between a vehicle and its tires can effect handling, braking, rollover, and other operational characteristics. Excess tire pressure can negatively impact performance and may increase susceptibility to puncture. Low tire pressure can negatively impact performance, increase wear, and may generate excess heat. It is therefore common for automotive tires to be designed for operation within a range of tire pressures based on tire type, vehicle type, vehicle configuration and use, and driving conditions.
In light of the role that tire pressure plays in vehicle performance, considerable desire has existed for keeping automotive tires within their preferred range of tire pressures. It is known that tires often lose air pressure during operation of the vehicle. Tire pressure is often lost gradually, making the point at which inflation pressure drops below the desired pressure range difficult to detect. Manually operated tire pressure gauges are often unreliable and inaccurate. Furthermore, the time and effort required for consumers to actively monitor their tire pressure using such manually operated gauges often renders them unused. It is therefore known that remote sensors may be placed inside the individual tires, or in communication with the valve stems, such that the tire pressure of each tire can be automatically relayed to the passenger compartment without the need of manual gauges.
In-vehicle tire-pressure monitoring systems often present considerable design challenges to automotive designers. Many known systems monitor tire pressures without regard to the individual identity or location of the tire whose pressure has varied from the preferred range. Thus when a tire varies in such a fashion, the operator is warned of a low tire pressure but must still utilize a manual gauge to locate and adjust the offending tire. Other systems associate a reported tire pressure with the i.d. of the sensor and therefore can provide both value and location information. These systems, however, are susceptible to commonplace automotive maintenance procedures such as tire rotation and replacement. As tire replacement or rotations are performed, the location of the pressure sensors may no longer conform to the indicated location of the tire pressure as indicated by the monitoring system.
Present approaches to accommodating the addition and rotation of tires utilize various forms of learning to determine the location of each transmitter/tire. Manual learning interfaces often require extensive customer interfaces in order to teach the receiver module the location of new or displaced sensor ids. This methodology requires undesirable customer effort and may be prone to errors. Automated learning interfaces eliminate both effort and error factors, but presently do so at the expense of undesirably added cost and weight. One approach utilizes low frequency transmitters mounted in each wheel well of the vehicle. These transmitters trigger the individual wheel mounted sensors. The receiver module is then used to associate a tire/transmitter id with a vehicle location by triggering each location and capturing the data received. This is an expensive solution and adds additional weight to the vehicle by positioning a transmitter in each wheel well.
It would therefore be highly desirable to have an automotive tire pressure monitoring assembly that eliminated the effort and error associated with manual learning interfaces. It would further be highly desirable to develop an automotive pressure monitoring assembly with that reduced the cost and weight associated with existing automated learning interfaces.